Overview

Dataset statistics

Number of variables59
Number of observations30000
Missing cells0
Missing cells (%)0.0%
Duplicate rows35
Duplicate rows (%)0.1%
Total size in memory9.3 MiB
Average record size in memory326.0 B

Variable types

Numeric26
Categorical33

Alerts

Dataset has 35 (0.1%) duplicate rowsDuplicates
PAY_SEP is highly correlated with PAY_AUG and 6 other fieldsHigh correlation
PAY_AUG is highly correlated with PAY_SEP and 13 other fieldsHigh correlation
PAY_JUL is highly correlated with PAY_SEP and 14 other fieldsHigh correlation
PAY_JUN is highly correlated with PAY_SEP and 16 other fieldsHigh correlation
PAY_MAY is highly correlated with PAY_AUG and 15 other fieldsHigh correlation
PAY_APR is highly correlated with PAY_AUG and 14 other fieldsHigh correlation
BIL_AMT_SEP is highly correlated with PAY_AUG and 14 other fieldsHigh correlation
BIL_AMT_AUG is highly correlated with PAY_AUG and 16 other fieldsHigh correlation
BIL_AMT_JUL is highly correlated with PAY_AUG and 17 other fieldsHigh correlation
BIL_AMT_JUN is highly correlated with PAY_JUL and 19 other fieldsHigh correlation
BIL_AMT_MAY is highly correlated with PAY_JUL and 18 other fieldsHigh correlation
BIL_AMT_APR is highly correlated with PAY_JUN and 13 other fieldsHigh correlation
PAY_AMT_SEP is highly correlated with BIL_AMT_SEP and 5 other fieldsHigh correlation
PAY_AMT_AUG is highly correlated with BIL_AMT_JUL and 5 other fieldsHigh correlation
PAY_AMT_JUL is highly correlated with BIL_AMT_JUN and 7 other fieldsHigh correlation
PAY_AMT_JUN is highly correlated with BIL_AMT_JUN and 6 other fieldsHigh correlation
PAY_AMT_MAY is highly correlated with BIL_AMT_JUN and 5 other fieldsHigh correlation
PAY_AMT_APR is highly correlated with BIL_AMT_MAY and 4 other fieldsHigh correlation
SEX_female is highly correlated with SEX_maleHigh correlation
SEX_male is highly correlated with SEX_femaleHigh correlation
EDUCATION_graduate school is highly correlated with EDUCATION_universityHigh correlation
EDUCATION_university is highly correlated with EDUCATION_graduate schoolHigh correlation
MARRIAGE_married is highly correlated with MARRIAGE_singleHigh correlation
MARRIAGE_single is highly correlated with MARRIAGE_marriedHigh correlation
NO_CONS_APR is highly correlated with PAY_JUN and 9 other fieldsHigh correlation
PAID_FULL_APR is highly correlated with REVOLVING_USE_APR and 4 other fieldsHigh correlation
REVOLVING_USE_APR is highly correlated with PAY_APR and 11 other fieldsHigh correlation
NO_CONS_MAY is highly correlated with PAY_JUL and 10 other fieldsHigh correlation
PAID_FULL_MAY is highly correlated with PAID_FULL_APR and 4 other fieldsHigh correlation
REVOLVING_USE_MAY is highly correlated with PAY_MAY and 11 other fieldsHigh correlation
NO_CONS_JUN is highly correlated with PAY_AUG and 11 other fieldsHigh correlation
PAID_FULL_JUN is highly correlated with PAID_FULL_APR and 5 other fieldsHigh correlation
REVOLVING_USE_JUN is highly correlated with BIL_AMT_SEP and 10 other fieldsHigh correlation
NO_CONS_JUL is highly correlated with PAY_AUG and 9 other fieldsHigh correlation
PAID_FULL_JUL is highly correlated with PAID_FULL_APR and 5 other fieldsHigh correlation
REVOLVING_USE_JUL is highly correlated with BIL_AMT_SEP and 9 other fieldsHigh correlation
NO_CONS_AUG is highly correlated with PAY_AUG and 8 other fieldsHigh correlation
PAID_FULL_AUG is highly correlated with PAY_AUG and 6 other fieldsHigh correlation
REVOLVING_USE_AUG is highly correlated with BIL_AMT_SEP and 8 other fieldsHigh correlation
NO_CONS_SEP is highly correlated with PAY_SEP and 6 other fieldsHigh correlation
PAID_FULL_SEP is highly correlated with PAY_SEP and 3 other fieldsHigh correlation
REVOLVING_USE_SEP is highly correlated with BIL_AMT_SEP and 6 other fieldsHigh correlation
PAY_DELAY_APR is highly correlated with PAY_APR and 2 other fieldsHigh correlation
PAY_DELAY_MAY is highly correlated with PAY_MAY and 2 other fieldsHigh correlation
PAY_DELAY_JUN is highly correlated with PAY_JUN and 3 other fieldsHigh correlation
PAY_DELAY_JUL is highly correlated with PAY_JUL and 2 other fieldsHigh correlation
PAY_DELAY_AUG is highly correlated with PAY_SEP and 3 other fieldsHigh correlation
PAY_DELAY_SEP is highly correlated with PAY_SEP and 2 other fieldsHigh correlation
PAY_SEP is highly correlated with PAY_AUG and 6 other fieldsHigh correlation
PAY_AUG is highly correlated with PAY_SEP and 9 other fieldsHigh correlation
PAY_JUL is highly correlated with PAY_SEP and 10 other fieldsHigh correlation
PAY_JUN is highly correlated with PAY_SEP and 12 other fieldsHigh correlation
PAY_MAY is highly correlated with PAY_SEP and 11 other fieldsHigh correlation
PAY_APR is highly correlated with PAY_AUG and 8 other fieldsHigh correlation
BIL_AMT_SEP is highly correlated with BIL_AMT_AUG and 4 other fieldsHigh correlation
BIL_AMT_AUG is highly correlated with BIL_AMT_SEP and 4 other fieldsHigh correlation
BIL_AMT_JUL is highly correlated with BIL_AMT_SEP and 4 other fieldsHigh correlation
BIL_AMT_JUN is highly correlated with BIL_AMT_SEP and 4 other fieldsHigh correlation
BIL_AMT_MAY is highly correlated with BIL_AMT_SEP and 4 other fieldsHigh correlation
BIL_AMT_APR is highly correlated with BIL_AMT_SEP and 4 other fieldsHigh correlation
SEX_female is highly correlated with SEX_maleHigh correlation
SEX_male is highly correlated with SEX_femaleHigh correlation
EDUCATION_graduate school is highly correlated with EDUCATION_universityHigh correlation
EDUCATION_university is highly correlated with EDUCATION_graduate schoolHigh correlation
MARRIAGE_married is highly correlated with MARRIAGE_singleHigh correlation
MARRIAGE_single is highly correlated with MARRIAGE_marriedHigh correlation
NO_CONS_APR is highly correlated with PAY_JUN and 7 other fieldsHigh correlation
PAID_FULL_APR is highly correlated with REVOLVING_USE_APR and 4 other fieldsHigh correlation
REVOLVING_USE_APR is highly correlated with PAID_FULL_APR and 4 other fieldsHigh correlation
NO_CONS_MAY is highly correlated with PAY_JUN and 7 other fieldsHigh correlation
PAID_FULL_MAY is highly correlated with PAID_FULL_APR and 4 other fieldsHigh correlation
REVOLVING_USE_MAY is highly correlated with REVOLVING_USE_APR and 5 other fieldsHigh correlation
NO_CONS_JUN is highly correlated with PAY_JUL and 8 other fieldsHigh correlation
PAID_FULL_JUN is highly correlated with PAID_FULL_APR and 5 other fieldsHigh correlation
REVOLVING_USE_JUN is highly correlated with REVOLVING_USE_APR and 5 other fieldsHigh correlation
NO_CONS_JUL is highly correlated with PAY_AUG and 8 other fieldsHigh correlation
PAID_FULL_JUL is highly correlated with PAID_FULL_APR and 5 other fieldsHigh correlation
REVOLVING_USE_JUL is highly correlated with REVOLVING_USE_APR and 5 other fieldsHigh correlation
NO_CONS_AUG is highly correlated with PAY_AUG and 6 other fieldsHigh correlation
PAID_FULL_AUG is highly correlated with PAID_FULL_APR and 5 other fieldsHigh correlation
REVOLVING_USE_AUG is highly correlated with REVOLVING_USE_APR and 5 other fieldsHigh correlation
NO_CONS_SEP is highly correlated with PAY_SEP and 5 other fieldsHigh correlation
PAID_FULL_SEP is highly correlated with PAID_FULL_JUN and 2 other fieldsHigh correlation
REVOLVING_USE_SEP is highly correlated with REVOLVING_USE_MAY and 3 other fieldsHigh correlation
PAY_DELAY_APR is highly correlated with PAY_JUN and 4 other fieldsHigh correlation
PAY_DELAY_MAY is highly correlated with PAY_JUN and 5 other fieldsHigh correlation
PAY_DELAY_JUN is highly correlated with PAY_JUL and 6 other fieldsHigh correlation
PAY_DELAY_JUL is highly correlated with PAY_AUG and 6 other fieldsHigh correlation
PAY_DELAY_AUG is highly correlated with PAY_SEP and 5 other fieldsHigh correlation
PAY_DELAY_SEP is highly correlated with PAY_SEP and 3 other fieldsHigh correlation
PAY_SEP is highly correlated with PAY_AUG and 3 other fieldsHigh correlation
PAY_AUG is highly correlated with PAY_SEP and 7 other fieldsHigh correlation
PAY_JUL is highly correlated with PAY_SEP and 8 other fieldsHigh correlation
PAY_JUN is highly correlated with PAY_AUG and 9 other fieldsHigh correlation
PAY_MAY is highly correlated with PAY_AUG and 9 other fieldsHigh correlation
PAY_APR is highly correlated with PAY_AUG and 10 other fieldsHigh correlation
BIL_AMT_SEP is highly correlated with BIL_AMT_AUG and 4 other fieldsHigh correlation
BIL_AMT_AUG is highly correlated with BIL_AMT_SEP and 6 other fieldsHigh correlation
BIL_AMT_JUL is highly correlated with BIL_AMT_SEP and 6 other fieldsHigh correlation
BIL_AMT_JUN is highly correlated with PAY_MAY and 5 other fieldsHigh correlation
BIL_AMT_MAY is highly correlated with PAY_APR and 6 other fieldsHigh correlation
BIL_AMT_APR is highly correlated with PAY_APR and 6 other fieldsHigh correlation
PAY_AMT_SEP is highly correlated with BIL_AMT_AUGHigh correlation
PAY_AMT_AUG is highly correlated with BIL_AMT_JULHigh correlation
PAY_AMT_JUN is highly correlated with BIL_AMT_MAYHigh correlation
PAY_AMT_MAY is highly correlated with BIL_AMT_APRHigh correlation
SEX_female is highly correlated with SEX_maleHigh correlation
SEX_male is highly correlated with SEX_femaleHigh correlation
EDUCATION_graduate school is highly correlated with EDUCATION_universityHigh correlation
EDUCATION_university is highly correlated with EDUCATION_graduate schoolHigh correlation
MARRIAGE_married is highly correlated with MARRIAGE_singleHigh correlation
MARRIAGE_single is highly correlated with MARRIAGE_marriedHigh correlation
NO_CONS_APR is highly correlated with PAY_JUN and 7 other fieldsHigh correlation
PAID_FULL_APR is highly correlated with REVOLVING_USE_APR and 4 other fieldsHigh correlation
REVOLVING_USE_APR is highly correlated with PAID_FULL_APR and 4 other fieldsHigh correlation
NO_CONS_MAY is highly correlated with PAY_JUN and 7 other fieldsHigh correlation
PAID_FULL_MAY is highly correlated with PAID_FULL_APR and 4 other fieldsHigh correlation
REVOLVING_USE_MAY is highly correlated with REVOLVING_USE_APR and 5 other fieldsHigh correlation
NO_CONS_JUN is highly correlated with PAY_JUL and 8 other fieldsHigh correlation
PAID_FULL_JUN is highly correlated with PAID_FULL_APR and 5 other fieldsHigh correlation
REVOLVING_USE_JUN is highly correlated with BIL_AMT_JUL and 6 other fieldsHigh correlation
NO_CONS_JUL is highly correlated with PAY_AUG and 9 other fieldsHigh correlation
PAID_FULL_JUL is highly correlated with PAID_FULL_APR and 5 other fieldsHigh correlation
REVOLVING_USE_JUL is highly correlated with BIL_AMT_AUG and 6 other fieldsHigh correlation
NO_CONS_AUG is highly correlated with PAY_AUG and 7 other fieldsHigh correlation
PAID_FULL_AUG is highly correlated with PAID_FULL_APR and 5 other fieldsHigh correlation
REVOLVING_USE_AUG is highly correlated with REVOLVING_USE_APR and 5 other fieldsHigh correlation
NO_CONS_SEP is highly correlated with NO_CONS_APR and 4 other fieldsHigh correlation
PAID_FULL_SEP is highly correlated with PAY_SEP and 3 other fieldsHigh correlation
REVOLVING_USE_SEP is highly correlated with REVOLVING_USE_MAY and 4 other fieldsHigh correlation
PAY_DELAY_APR is highly correlated with PAY_APR and 2 other fieldsHigh correlation
PAY_DELAY_MAY is highly correlated with PAY_MAY and 2 other fieldsHigh correlation
PAY_DELAY_JUN is highly correlated with PAY_JUN and 3 other fieldsHigh correlation
PAY_DELAY_JUL is highly correlated with PAY_JUL and 2 other fieldsHigh correlation
PAY_DELAY_AUG is highly correlated with PAY_AUG and 2 other fieldsHigh correlation
PAY_DELAY_SEP is highly correlated with PAY_SEP and 2 other fieldsHigh correlation
NO_CONS_SEP is highly correlated with NO_CONS_JUL and 4 other fieldsHigh correlation
REVOLVING_USE_APR is highly correlated with REVOLVING_USE_AUG and 4 other fieldsHigh correlation
PAID_FULL_JUN is highly correlated with PAID_FULL_SEP and 5 other fieldsHigh correlation
REVOLVING_USE_AUG is highly correlated with REVOLVING_USE_APR and 5 other fieldsHigh correlation
PAID_FULL_SEP is highly correlated with PAID_FULL_JUN and 2 other fieldsHigh correlation
REVOLVING_USE_SEP is highly correlated with REVOLVING_USE_AUG and 3 other fieldsHigh correlation
EDUCATION_university is highly correlated with EDUCATION_graduate school and 1 other fieldsHigh correlation
REVOLVING_USE_MAY is highly correlated with REVOLVING_USE_APR and 5 other fieldsHigh correlation
MARRIAGE_divorce is highly correlated with MARRIAGE_VHigh correlation
MARRIAGE_single is highly correlated with MARRIAGE_married and 1 other fieldsHigh correlation
EDUCATION_graduate school is highly correlated with EDUCATION_university and 1 other fieldsHigh correlation
DEFAULT is highly correlated with DEFAULT_NUMHigh correlation
SEX_male is highly correlated with SEX_V and 1 other fieldsHigh correlation
SEX_V is highly correlated with SEX_male and 1 other fieldsHigh correlation
PAID_FULL_MAY is highly correlated with PAID_FULL_JUN and 4 other fieldsHigh correlation
REVOLVING_USE_JUL is highly correlated with REVOLVING_USE_APR and 5 other fieldsHigh correlation
MARRIAGE_married is highly correlated with MARRIAGE_single and 1 other fieldsHigh correlation
NO_CONS_JUL is highly correlated with NO_CONS_SEP and 4 other fieldsHigh correlation
NO_CONS_APR is highly correlated with NO_CONS_SEP and 4 other fieldsHigh correlation
NO_CONS_AUG is highly correlated with NO_CONS_SEP and 4 other fieldsHigh correlation
EDUCATION_other is highly correlated with EDUCATION_VHigh correlation
PAID_FULL_AUG is highly correlated with PAID_FULL_JUN and 5 other fieldsHigh correlation
REVOLVING_USE_JUN is highly correlated with REVOLVING_USE_APR and 5 other fieldsHigh correlation
MARRIAGE_V is highly correlated with MARRIAGE_divorce and 3 other fieldsHigh correlation
EDUCATION_high school is highly correlated with EDUCATION_VHigh correlation
PAID_FULL_JUL is highly correlated with PAID_FULL_JUN and 5 other fieldsHigh correlation
NO_CONS_MAY is highly correlated with NO_CONS_SEP and 4 other fieldsHigh correlation
EDUCATION_V is highly correlated with EDUCATION_university and 3 other fieldsHigh correlation
PAID_FULL_APR is highly correlated with REVOLVING_USE_APR and 4 other fieldsHigh correlation
MARRIAGE_others is highly correlated with MARRIAGE_VHigh correlation
SEX_female is highly correlated with SEX_male and 1 other fieldsHigh correlation
DEFAULT_NUM is highly correlated with DEFAULTHigh correlation
NO_CONS_JUN is highly correlated with NO_CONS_SEP and 4 other fieldsHigh correlation
LIMIT_BAL is highly correlated with BIL_AMT_SEP and 5 other fieldsHigh correlation
AGE is highly correlated with MARRIAGE_married and 1 other fieldsHigh correlation
PAY_SEP is highly correlated with PAY_AUG and 28 other fieldsHigh correlation
PAY_AUG is highly correlated with PAY_SEP and 26 other fieldsHigh correlation
PAY_JUL is highly correlated with PAY_SEP and 28 other fieldsHigh correlation
PAY_JUN is highly correlated with PAY_SEP and 28 other fieldsHigh correlation
PAY_MAY is highly correlated with PAY_SEP and 28 other fieldsHigh correlation
PAY_APR is highly correlated with PAY_SEP and 28 other fieldsHigh correlation
BIL_AMT_SEP is highly correlated with LIMIT_BAL and 6 other fieldsHigh correlation
BIL_AMT_AUG is highly correlated with LIMIT_BAL and 6 other fieldsHigh correlation
BIL_AMT_JUL is highly correlated with BIL_AMT_SEP and 6 other fieldsHigh correlation
BIL_AMT_JUN is highly correlated with LIMIT_BAL and 8 other fieldsHigh correlation
BIL_AMT_MAY is highly correlated with LIMIT_BAL and 8 other fieldsHigh correlation
BIL_AMT_APR is highly correlated with LIMIT_BAL and 6 other fieldsHigh correlation
PAY_AMT_SEP is highly correlated with PAY_AMT_AUG and 2 other fieldsHigh correlation
PAY_AMT_AUG is highly correlated with BIL_AMT_JUL and 3 other fieldsHigh correlation
PAY_AMT_JUL is highly correlated with LIMIT_BAL and 8 other fieldsHigh correlation
PAY_AMT_JUN is highly correlated with PAY_AMT_SEP and 1 other fieldsHigh correlation
PAY_AMT_MAY is highly correlated with BIL_AMT_JUL and 1 other fieldsHigh correlation
DEFAULT is highly correlated with PAY_SEP and 1 other fieldsHigh correlation
SEX_V is highly correlated with SEX_female and 1 other fieldsHigh correlation
EDUCATION_V is highly correlated with EDUCATION_graduate school and 3 other fieldsHigh correlation
MARRIAGE_V is highly correlated with MARRIAGE_divorce and 3 other fieldsHigh correlation
SEX_female is highly correlated with SEX_V and 1 other fieldsHigh correlation
SEX_male is highly correlated with SEX_V and 1 other fieldsHigh correlation
EDUCATION_graduate school is highly correlated with EDUCATION_V and 1 other fieldsHigh correlation
EDUCATION_high school is highly correlated with EDUCATION_V and 1 other fieldsHigh correlation
EDUCATION_other is highly correlated with EDUCATION_VHigh correlation
EDUCATION_university is highly correlated with EDUCATION_V and 2 other fieldsHigh correlation
MARRIAGE_divorce is highly correlated with MARRIAGE_VHigh correlation
MARRIAGE_married is highly correlated with AGE and 2 other fieldsHigh correlation
MARRIAGE_others is highly correlated with MARRIAGE_VHigh correlation
MARRIAGE_single is highly correlated with AGE and 2 other fieldsHigh correlation
DEFAULT_NUM is highly correlated with PAY_SEP and 1 other fieldsHigh correlation
NO_CONS_APR is highly correlated with PAY_SEP and 14 other fieldsHigh correlation
PAID_FULL_APR is highly correlated with PAY_SEP and 12 other fieldsHigh correlation
REVOLVING_USE_APR is highly correlated with PAY_SEP and 18 other fieldsHigh correlation
NO_CONS_MAY is highly correlated with PAY_SEP and 14 other fieldsHigh correlation
PAID_FULL_MAY is highly correlated with PAY_SEP and 14 other fieldsHigh correlation
REVOLVING_USE_MAY is highly correlated with PAY_SEP and 20 other fieldsHigh correlation
NO_CONS_JUN is highly correlated with PAY_SEP and 15 other fieldsHigh correlation
PAID_FULL_JUN is highly correlated with PAY_SEP and 14 other fieldsHigh correlation
REVOLVING_USE_JUN is highly correlated with PAY_SEP and 19 other fieldsHigh correlation
NO_CONS_JUL is highly correlated with PAY_SEP and 16 other fieldsHigh correlation
PAID_FULL_JUL is highly correlated with PAY_SEP and 14 other fieldsHigh correlation
REVOLVING_USE_JUL is highly correlated with PAY_SEP and 20 other fieldsHigh correlation
NO_CONS_AUG is highly correlated with PAY_SEP and 16 other fieldsHigh correlation
PAID_FULL_AUG is highly correlated with PAY_SEP and 14 other fieldsHigh correlation
REVOLVING_USE_AUG is highly correlated with PAY_SEP and 18 other fieldsHigh correlation
NO_CONS_SEP is highly correlated with PAY_SEP and 11 other fieldsHigh correlation
PAID_FULL_SEP is highly correlated with PAY_SEP and 11 other fieldsHigh correlation
REVOLVING_USE_SEP is highly correlated with PAY_SEP and 16 other fieldsHigh correlation
PAY_DELAY_APR is highly correlated with PAY_JUL and 6 other fieldsHigh correlation
PAY_DELAY_MAY is highly correlated with PAY_JUL and 8 other fieldsHigh correlation
PAY_DELAY_JUN is highly correlated with PAY_SEP and 10 other fieldsHigh correlation
PAY_DELAY_JUL is highly correlated with PAY_SEP and 10 other fieldsHigh correlation
PAY_DELAY_AUG is highly correlated with PAY_SEP and 9 other fieldsHigh correlation
PAY_DELAY_SEP is highly correlated with PAY_SEP and 9 other fieldsHigh correlation
PAY_AMT_AUG is highly skewed (γ1 = 30.45381745) Skewed
PAY_SEP has 14737 (49.1%) zeros Zeros
PAY_AUG has 15730 (52.4%) zeros Zeros
PAY_JUL has 15764 (52.5%) zeros Zeros
PAY_JUN has 16455 (54.9%) zeros Zeros
PAY_MAY has 16947 (56.5%) zeros Zeros
PAY_APR has 16286 (54.3%) zeros Zeros
BIL_AMT_SEP has 2008 (6.7%) zeros Zeros
BIL_AMT_AUG has 2506 (8.4%) zeros Zeros
BIL_AMT_JUL has 2870 (9.6%) zeros Zeros
BIL_AMT_JUN has 3195 (10.7%) zeros Zeros
BIL_AMT_MAY has 3506 (11.7%) zeros Zeros
BIL_AMT_APR has 4020 (13.4%) zeros Zeros
PAY_AMT_SEP has 5249 (17.5%) zeros Zeros
PAY_AMT_AUG has 5396 (18.0%) zeros Zeros
PAY_AMT_JUL has 5968 (19.9%) zeros Zeros
PAY_AMT_JUN has 6408 (21.4%) zeros Zeros
PAY_AMT_MAY has 6703 (22.3%) zeros Zeros
PAY_AMT_APR has 7173 (23.9%) zeros Zeros
PAY_DELAY_APR has 26921 (89.7%) zeros Zeros
PAY_DELAY_MAY has 27032 (90.1%) zeros Zeros
PAY_DELAY_JUN has 26490 (88.3%) zeros Zeros
PAY_DELAY_JUL has 25787 (86.0%) zeros Zeros
PAY_DELAY_AUG has 25562 (85.2%) zeros Zeros
PAY_DELAY_SEP has 23182 (77.3%) zeros Zeros

Reproduction

Analysis started2021-10-08 01:28:15.537026
Analysis finished2021-10-08 01:30:17.535031
Duration2 minutes and 2 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

LIMIT_BAL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.3227
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:17.584278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.6616
Coefficient of variation (CV)0.7746854124
Kurtosis0.5362628964
Mean167484.3227
Median Absolute Deviation (MAD)90000
Skewness0.9928669605
Sum5024529680
Variance1.683445568 × 1010
MonotonicityNot monotonic
2021-10-08T03:30:17.723000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500003365
 
11.2%
200001976
 
6.6%
300001610
 
5.4%
800001567
 
5.2%
2000001528
 
5.1%
1500001110
 
3.7%
1000001048
 
3.5%
180000995
 
3.3%
360000881
 
2.9%
60000825
 
2.8%
Other values (71)15095
50.3%
ValueCountFrequency (%)
10000493
 
1.6%
160002
 
< 0.1%
200001976
6.6%
300001610
5.4%
40000230
 
0.8%
500003365
11.2%
60000825
 
2.8%
70000731
 
2.4%
800001567
5.2%
90000651
 
2.2%
ValueCountFrequency (%)
10000001
 
< 0.1%
8000002
 
< 0.1%
7800002
 
< 0.1%
7600001
 
< 0.1%
7500004
< 0.1%
7400002
 
< 0.1%
7300002
 
< 0.1%
7200003
 
< 0.1%
7100006
< 0.1%
7000008
< 0.1%

AGE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4855
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:18.405718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.217904068
Coefficient of variation (CV)0.2597653709
Kurtosis0.04430337824
Mean35.4855
Median Absolute Deviation (MAD)6
Skewness0.7322458688
Sum1064565
Variance84.96975541
MonotonicityNot monotonic
2021-10-08T03:30:18.505991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291605
 
5.3%
271477
 
4.9%
281409
 
4.7%
301395
 
4.7%
261256
 
4.2%
311217
 
4.1%
251186
 
4.0%
341162
 
3.9%
321158
 
3.9%
331146
 
3.8%
Other values (46)16989
56.6%
ValueCountFrequency (%)
2167
 
0.2%
22560
 
1.9%
23931
3.1%
241127
3.8%
251186
4.0%
261256
4.2%
271477
4.9%
281409
4.7%
291605
5.3%
301395
4.7%
ValueCountFrequency (%)
791
 
< 0.1%
753
 
< 0.1%
741
 
< 0.1%
734
 
< 0.1%
723
 
< 0.1%
713
 
< 0.1%
7010
< 0.1%
6915
0.1%
685
 
< 0.1%
6716
0.1%

PAY_SEP
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0167
Minimum-2
Maximum8
Zeros14737
Zeros (%)49.1%
Negative8445
Negative (%)28.1%
Memory size234.5 KiB
2021-10-08T03:30:18.633120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.123801528
Coefficient of variation (CV)-67.29350467
Kurtosis2.720715042
Mean-0.0167
Median Absolute Deviation (MAD)1
Skewness0.7319749269
Sum-501
Variance1.262929874
MonotonicityNot monotonic
2021-10-08T03:30:18.727707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
014737
49.1%
-15686
 
19.0%
13688
 
12.3%
-22759
 
9.2%
22667
 
8.9%
3322
 
1.1%
476
 
0.3%
526
 
0.1%
819
 
0.1%
611
 
< 0.1%
ValueCountFrequency (%)
-22759
 
9.2%
-15686
 
19.0%
014737
49.1%
13688
 
12.3%
22667
 
8.9%
3322
 
1.1%
476
 
0.3%
526
 
0.1%
611
 
< 0.1%
79
 
< 0.1%
ValueCountFrequency (%)
819
 
0.1%
79
 
< 0.1%
611
 
< 0.1%
526
 
0.1%
476
 
0.3%
3322
 
1.1%
22667
 
8.9%
13688
 
12.3%
014737
49.1%
-15686
 
19.0%

PAY_AUG
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1337666667
Minimum-2
Maximum8
Zeros15730
Zeros (%)52.4%
Negative9832
Negative (%)32.8%
Memory size234.5 KiB
2021-10-08T03:30:18.806824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.197185973
Coefficient of variation (CV)-8.949807922
Kurtosis1.57041773
Mean-0.1337666667
Median Absolute Deviation (MAD)0
Skewness0.7905650222
Sum-4013
Variance1.433254254
MonotonicityNot monotonic
2021-10-08T03:30:18.907182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
015730
52.4%
-16050
 
20.2%
23927
 
13.1%
-23782
 
12.6%
3326
 
1.1%
499
 
0.3%
128
 
0.1%
525
 
0.1%
720
 
0.1%
612
 
< 0.1%
ValueCountFrequency (%)
-23782
 
12.6%
-16050
 
20.2%
015730
52.4%
128
 
0.1%
23927
 
13.1%
3326
 
1.1%
499
 
0.3%
525
 
0.1%
612
 
< 0.1%
720
 
0.1%
ValueCountFrequency (%)
81
 
< 0.1%
720
 
0.1%
612
 
< 0.1%
525
 
0.1%
499
 
0.3%
3326
 
1.1%
23927
 
13.1%
128
 
0.1%
015730
52.4%
-16050
 
20.2%

PAY_JUL
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1662
Minimum-2
Maximum8
Zeros15764
Zeros (%)52.5%
Negative10023
Negative (%)33.4%
Memory size234.5 KiB
2021-10-08T03:30:19.007365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.196867568
Coefficient of variation (CV)-7.201369245
Kurtosis2.084435875
Mean-0.1662
Median Absolute Deviation (MAD)0
Skewness0.8406818269
Sum-4986
Variance1.432491976
MonotonicityNot monotonic
2021-10-08T03:30:19.107889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
015764
52.5%
-15938
 
19.8%
-24085
 
13.6%
23819
 
12.7%
3240
 
0.8%
476
 
0.3%
727
 
0.1%
623
 
0.1%
521
 
0.1%
14
 
< 0.1%
ValueCountFrequency (%)
-24085
 
13.6%
-15938
 
19.8%
015764
52.5%
14
 
< 0.1%
23819
 
12.7%
3240
 
0.8%
476
 
0.3%
521
 
0.1%
623
 
0.1%
727
 
0.1%
ValueCountFrequency (%)
83
 
< 0.1%
727
 
0.1%
623
 
0.1%
521
 
0.1%
476
 
0.3%
3240
 
0.8%
23819
 
12.7%
14
 
< 0.1%
015764
52.5%
-15938
 
19.8%

PAY_JUN
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2206666667
Minimum-2
Maximum8
Zeros16455
Zeros (%)54.9%
Negative10035
Negative (%)33.5%
Memory size234.5 KiB
2021-10-08T03:30:19.205074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.169138622
Coefficient of variation (CV)-5.29821128
Kurtosis3.496983496
Mean-0.2206666667
Median Absolute Deviation (MAD)0
Skewness0.9996294133
Sum-6620
Variance1.366885118
MonotonicityNot monotonic
2021-10-08T03:30:19.296831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
016455
54.9%
-15687
 
19.0%
-24348
 
14.5%
23159
 
10.5%
3180
 
0.6%
469
 
0.2%
758
 
0.2%
535
 
0.1%
65
 
< 0.1%
12
 
< 0.1%
ValueCountFrequency (%)
-24348
 
14.5%
-15687
 
19.0%
016455
54.9%
12
 
< 0.1%
23159
 
10.5%
3180
 
0.6%
469
 
0.2%
535
 
0.1%
65
 
< 0.1%
758
 
0.2%
ValueCountFrequency (%)
82
 
< 0.1%
758
 
0.2%
65
 
< 0.1%
535
 
0.1%
469
 
0.2%
3180
 
0.6%
23159
 
10.5%
12
 
< 0.1%
016455
54.9%
-15687
 
19.0%

PAY_MAY
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2662
Minimum-2
Maximum8
Zeros16947
Zeros (%)56.5%
Negative10085
Negative (%)33.6%
Memory size234.5 KiB
2021-10-08T03:30:19.387985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.133187406
Coefficient of variation (CV)-4.256902352
Kurtosis3.989748144
Mean-0.2662
Median Absolute Deviation (MAD)0
Skewness1.008197025
Sum-7986
Variance1.284113697
MonotonicityNot monotonic
2021-10-08T03:30:19.487445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
016947
56.5%
-15539
 
18.5%
-24546
 
15.2%
22626
 
8.8%
3178
 
0.6%
484
 
0.3%
758
 
0.2%
517
 
0.1%
64
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
-24546
 
15.2%
-15539
 
18.5%
016947
56.5%
22626
 
8.8%
3178
 
0.6%
484
 
0.3%
517
 
0.1%
64
 
< 0.1%
758
 
0.2%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
758
 
0.2%
64
 
< 0.1%
517
 
0.1%
484
 
0.3%
3178
 
0.6%
22626
 
8.8%
016947
56.5%
-15539
 
18.5%
-24546
 
15.2%

PAY_APR
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2911
Minimum-2
Maximum8
Zeros16286
Zeros (%)54.3%
Negative10635
Negative (%)35.4%
Memory size234.5 KiB
2021-10-08T03:30:19.587382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.149987626
Coefficient of variation (CV)-3.950489954
Kurtosis3.42653413
Mean-0.2911
Median Absolute Deviation (MAD)0
Skewness0.9480293916
Sum-8733
Variance1.322471539
MonotonicityNot monotonic
2021-10-08T03:30:19.687385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
016286
54.3%
-15740
 
19.1%
-24895
 
16.3%
22766
 
9.2%
3184
 
0.6%
449
 
0.2%
746
 
0.2%
619
 
0.1%
513
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
-24895
 
16.3%
-15740
 
19.1%
016286
54.3%
22766
 
9.2%
3184
 
0.6%
449
 
0.2%
513
 
< 0.1%
619
 
0.1%
746
 
0.2%
82
 
< 0.1%
ValueCountFrequency (%)
82
 
< 0.1%
746
 
0.2%
619
 
0.1%
513
 
< 0.1%
449
 
0.2%
3184
 
0.6%
22766
 
9.2%
016286
54.3%
-15740
 
19.1%
-24895
 
16.3%

BIL_AMT_SEP
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22723
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.3309
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Negative590
Negative (%)2.0%
Memory size234.5 KiB
2021-10-08T03:30:19.789126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.86058
Coefficient of variation (CV)1.437545339
Kurtosis9.806289341
Mean51223.3309
Median Absolute Deviation (MAD)21800.5
Skewness2.663861022
Sum1536699927
Variance5422239963
MonotonicityNot monotonic
2021-10-08T03:30:19.919085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02008
 
6.7%
390244
 
0.8%
78076
 
0.3%
32672
 
0.2%
31663
 
0.2%
250059
 
0.2%
39649
 
0.2%
240039
 
0.1%
41629
 
0.1%
50025
 
0.1%
Other values (22713)27336
91.1%
ValueCountFrequency (%)
-1655801
< 0.1%
-1549731
< 0.1%
-153081
< 0.1%
-143861
< 0.1%
-115451
< 0.1%
-106821
< 0.1%
-98021
< 0.1%
-90951
< 0.1%
-81871
< 0.1%
-74381
< 0.1%
ValueCountFrequency (%)
9645111
< 0.1%
7468141
< 0.1%
6530621
< 0.1%
6304581
< 0.1%
6266481
< 0.1%
6217491
< 0.1%
6138601
< 0.1%
6107231
< 0.1%
6085941
< 0.1%
6040191
< 0.1%

BIL_AMT_AUG
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22346
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.07517
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Negative669
Negative (%)2.2%
Memory size234.5 KiB
2021-10-08T03:30:20.055236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.76878
Coefficient of variation (CV)1.447236829
Kurtosis10.30294592
Mean49179.07517
Median Absolute Deviation (MAD)20810
Skewness2.705220853
Sum1475372255
Variance5065705363
MonotonicityNot monotonic
2021-10-08T03:30:20.181901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02506
 
8.4%
390231
 
0.8%
32675
 
0.2%
78075
 
0.2%
31672
 
0.2%
39651
 
0.2%
250051
 
0.2%
240042
 
0.1%
-20029
 
0.1%
41628
 
0.1%
Other values (22336)26840
89.5%
ValueCountFrequency (%)
-697771
< 0.1%
-675261
< 0.1%
-333501
< 0.1%
-300001
< 0.1%
-262141
< 0.1%
-247041
< 0.1%
-247021
< 0.1%
-229601
< 0.1%
-186181
< 0.1%
-180881
< 0.1%
ValueCountFrequency (%)
9839311
< 0.1%
7439701
< 0.1%
6715631
< 0.1%
6467701
< 0.1%
6244751
< 0.1%
6059431
< 0.1%
5977931
< 0.1%
5868251
< 0.1%
5817751
< 0.1%
5776811
< 0.1%

BIL_AMT_JUL
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22026
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.1548
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2021-10-08T03:30:20.307810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.38743
Coefficient of variation (CV)1.475106015
Kurtosis19.78325514
Mean47013.1548
Median Absolute Deviation (MAD)19708.5
Skewness3.087830046
Sum1410394644
Variance4809337537
MonotonicityNot monotonic
2021-10-08T03:30:20.428746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02870
 
9.6%
390275
 
0.9%
78074
 
0.2%
32663
 
0.2%
31662
 
0.2%
39648
 
0.2%
250040
 
0.1%
240039
 
0.1%
41629
 
0.1%
20027
 
0.1%
Other values (22016)26473
88.2%
ValueCountFrequency (%)
-1572641
< 0.1%
-615061
< 0.1%
-461271
< 0.1%
-340411
< 0.1%
-254431
< 0.1%
-247021
< 0.1%
-203201
< 0.1%
-177061
< 0.1%
-159101
< 0.1%
-156411
< 0.1%
ValueCountFrequency (%)
16640891
< 0.1%
8550861
< 0.1%
6931311
< 0.1%
6896431
< 0.1%
6896271
< 0.1%
6320411
< 0.1%
5974151
< 0.1%
5789711
< 0.1%
5779571
< 0.1%
5770151
< 0.1%

BIL_AMT_JUN
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21548
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.94897
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Negative675
Negative (%)2.2%
Memory size234.5 KiB
2021-10-08T03:30:20.557187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.85613
Coefficient of variation (CV)1.487019671
Kurtosis11.30932483
Mean43262.94897
Median Absolute Deviation (MAD)18656
Skewness2.821965291
Sum1297888469
Variance4138716378
MonotonicityNot monotonic
2021-10-08T03:30:20.687402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03195
 
10.7%
390246
 
0.8%
780101
 
0.3%
31668
 
0.2%
32662
 
0.2%
39644
 
0.1%
240039
 
0.1%
15039
 
0.1%
250034
 
0.1%
41633
 
0.1%
Other values (21538)26139
87.1%
ValueCountFrequency (%)
-1700001
< 0.1%
-813341
< 0.1%
-651671
< 0.1%
-506161
< 0.1%
-466271
< 0.1%
-345031
< 0.1%
-274901
< 0.1%
-243031
< 0.1%
-221081
< 0.1%
-203201
< 0.1%
ValueCountFrequency (%)
8915861
< 0.1%
7068641
< 0.1%
6286991
< 0.1%
6168361
< 0.1%
5728051
< 0.1%
5690341
< 0.1%
5656691
< 0.1%
5635431
< 0.1%
5480201
< 0.1%
5426531
< 0.1%

BIL_AMT_MAY
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21010
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.40097
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2021-10-08T03:30:20.818336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.15577
Coefficient of variation (CV)1.508187617
Kurtosis12.30588129
Mean40311.40097
Median Absolute Deviation (MAD)17688.5
Skewness2.876379867
Sum1209342029
Variance3696294150
MonotonicityNot monotonic
2021-10-08T03:30:20.951481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03506
 
11.7%
390235
 
0.8%
78094
 
0.3%
31679
 
0.3%
32662
 
0.2%
15058
 
0.2%
39647
 
0.2%
240039
 
0.1%
250037
 
0.1%
41636
 
0.1%
Other values (21000)25807
86.0%
ValueCountFrequency (%)
-813341
< 0.1%
-613721
< 0.1%
-530071
< 0.1%
-466271
< 0.1%
-375941
< 0.1%
-361561
< 0.1%
-304811
< 0.1%
-283351
< 0.1%
-230031
< 0.1%
-207531
< 0.1%
ValueCountFrequency (%)
9271711
< 0.1%
8235401
< 0.1%
5870671
< 0.1%
5517021
< 0.1%
5478801
< 0.1%
5306721
< 0.1%
5243151
< 0.1%
5161391
< 0.1%
5141141
< 0.1%
5082131
< 0.1%

BIL_AMT_APR
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20604
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.7604
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Negative688
Negative (%)2.3%
Memory size234.5 KiB
2021-10-08T03:30:21.093827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.10754
Coefficient of variation (CV)1.53206613
Kurtosis12.27070529
Mean38871.7604
Median Absolute Deviation (MAD)16755
Skewness2.846644576
Sum1166152812
Variance3546691724
MonotonicityNot monotonic
2021-10-08T03:30:21.250884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04020
 
13.4%
390207
 
0.7%
78086
 
0.3%
15078
 
0.3%
31677
 
0.3%
32656
 
0.2%
39645
 
0.1%
41636
 
0.1%
-1833
 
0.1%
240032
 
0.1%
Other values (20594)25330
84.4%
ValueCountFrequency (%)
-3396031
< 0.1%
-2090511
< 0.1%
-1509531
< 0.1%
-946251
< 0.1%
-738951
< 0.1%
-570601
< 0.1%
-514431
< 0.1%
-511831
< 0.1%
-466271
< 0.1%
-457341
< 0.1%
ValueCountFrequency (%)
9616641
< 0.1%
6999441
< 0.1%
5686381
< 0.1%
5277111
< 0.1%
5275661
< 0.1%
5149751
< 0.1%
5137981
< 0.1%
5119051
< 0.1%
5013701
< 0.1%
4991001
< 0.1%

PAY_AMT_SEP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:21.391354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28035
Coefficient of variation (CV)2.924524575
Kurtosis415.2547427
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.66836433
Sum169907415
Variance274342256.1
MonotonicityNot monotonic
2021-10-08T03:30:21.520565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05249
 
17.5%
20001363
 
4.5%
3000891
 
3.0%
5000698
 
2.3%
1500507
 
1.7%
4000426
 
1.4%
10000401
 
1.3%
1000365
 
1.2%
2500298
 
1.0%
6000294
 
1.0%
Other values (7933)19508
65.0%
ValueCountFrequency (%)
05249
17.5%
19
 
< 0.1%
214
 
< 0.1%
315
 
0.1%
418
 
0.1%
512
 
< 0.1%
615
 
0.1%
79
 
< 0.1%
88
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
8735521
< 0.1%
5050001
< 0.1%
4933581
< 0.1%
4239031
< 0.1%
4050161
< 0.1%
3681991
< 0.1%
3230141
< 0.1%
3048151
< 0.1%
3020001
< 0.1%
3000391
< 0.1%

PAY_AMT_AUG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7899
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:21.634180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.8704
Coefficient of variation (CV)3.891274139
Kurtosis1641.631911
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.45381745
Sum177634905
Variance530881708.9
MonotonicityNot monotonic
2021-10-08T03:30:21.766987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05396
 
18.0%
20001290
 
4.3%
3000857
 
2.9%
5000717
 
2.4%
1000594
 
2.0%
1500521
 
1.7%
4000410
 
1.4%
10000318
 
1.1%
6000283
 
0.9%
2500251
 
0.8%
Other values (7889)19363
64.5%
ValueCountFrequency (%)
05396
18.0%
115
 
0.1%
220
 
0.1%
318
 
0.1%
411
 
< 0.1%
525
 
0.1%
68
 
< 0.1%
712
 
< 0.1%
89
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
16842591
< 0.1%
12270821
< 0.1%
12154711
< 0.1%
10245161
< 0.1%
5804641
< 0.1%
4155521
< 0.1%
4010031
< 0.1%
3881261
< 0.1%
3852281
< 0.1%
3849861
< 0.1%

PAY_AMT_JUL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:21.914824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.96147
Coefficient of variation (CV)3.36931393
Kurtosis564.3112295
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.21663544
Sum156770445
Variance310005092.2
MonotonicityNot monotonic
2021-10-08T03:30:22.033527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05968
 
19.9%
20001285
 
4.3%
10001103
 
3.7%
3000870
 
2.9%
5000721
 
2.4%
1500490
 
1.6%
4000381
 
1.3%
10000312
 
1.0%
1200243
 
0.8%
6000241
 
0.8%
Other values (7508)18386
61.3%
ValueCountFrequency (%)
05968
19.9%
113
 
< 0.1%
219
 
0.1%
314
 
< 0.1%
415
 
0.1%
518
 
0.1%
614
 
< 0.1%
718
 
0.1%
810
 
< 0.1%
912
 
< 0.1%
ValueCountFrequency (%)
8960401
< 0.1%
8890431
< 0.1%
5082291
< 0.1%
4175881
< 0.1%
4009721
< 0.1%
3970921
< 0.1%
3804781
< 0.1%
3717181
< 0.1%
3493951
< 0.1%
3442611
< 0.1%

PAY_AMT_JUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6937
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.076867
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:22.165552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.15974
Coefficient of variation (CV)3.246147995
Kurtosis277.3337677
Mean4826.076867
Median Absolute Deviation (MAD)1500
Skewness12.90498482
Sum144782306
Variance245428561.1
MonotonicityNot monotonic
2021-10-08T03:30:22.285156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06408
 
21.4%
10001394
 
4.6%
20001214
 
4.0%
3000887
 
3.0%
5000810
 
2.7%
1500441
 
1.5%
4000402
 
1.3%
10000341
 
1.1%
2500259
 
0.9%
500258
 
0.9%
Other values (6927)17586
58.6%
ValueCountFrequency (%)
06408
21.4%
122
 
0.1%
222
 
0.1%
313
 
< 0.1%
420
 
0.1%
512
 
< 0.1%
616
 
0.1%
711
 
< 0.1%
87
 
< 0.1%
99
 
< 0.1%
ValueCountFrequency (%)
6210001
< 0.1%
5288971
< 0.1%
4970001
< 0.1%
4321301
< 0.1%
4000461
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3200081
< 0.1%
3130941
< 0.1%
2929621
< 0.1%

PAY_AMT_MAY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.387633
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:22.426538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.30568
Coefficient of variation (CV)3.183386475
Kurtosis180.0639402
Mean4799.387633
Median Absolute Deviation (MAD)1500
Skewness11.12741705
Sum143981629
Variance233426624.4
MonotonicityNot monotonic
2021-10-08T03:30:22.548501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06703
 
22.3%
10001340
 
4.5%
20001323
 
4.4%
3000947
 
3.2%
5000814
 
2.7%
1500426
 
1.4%
4000401
 
1.3%
10000343
 
1.1%
500250
 
0.8%
6000247
 
0.8%
Other values (6887)17206
57.4%
ValueCountFrequency (%)
06703
22.3%
121
 
0.1%
213
 
< 0.1%
313
 
< 0.1%
412
 
< 0.1%
59
 
< 0.1%
67
 
< 0.1%
79
 
< 0.1%
86
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
4265291
< 0.1%
4179901
< 0.1%
3880711
< 0.1%
3792671
< 0.1%
3320001
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3268891
< 0.1%
3170771
< 0.1%
3101351
< 0.1%

PAY_AMT_APR
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6939
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.502567
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:22.672651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.46578
Coefficient of variation (CV)3.408581541
Kurtosis167.1614296
Mean5215.502567
Median Absolute Deviation (MAD)1500
Skewness10.64072733
Sum156465077
Variance316038289.4
MonotonicityNot monotonic
2021-10-08T03:30:22.815053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07173
23.9%
10001299
 
4.3%
20001295
 
4.3%
3000914
 
3.0%
5000808
 
2.7%
1500439
 
1.5%
4000411
 
1.4%
10000356
 
1.2%
500247
 
0.8%
6000220
 
0.7%
Other values (6929)16838
56.1%
ValueCountFrequency (%)
07173
23.9%
120
 
0.1%
29
 
< 0.1%
314
 
< 0.1%
412
 
< 0.1%
57
 
< 0.1%
66
 
< 0.1%
75
 
< 0.1%
86
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
5286661
< 0.1%
5271431
< 0.1%
4430011
< 0.1%
4220001
< 0.1%
4035001
< 0.1%
3770001
< 0.1%
3724951
< 0.1%
3512821
< 0.1%
3452931
< 0.1%
3080001
< 0.1%

DEFAULT
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
not default
23364 
default
6636 

Length

Max length11
Median length11
Mean length10.1152
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdefault
2nd rowdefault
3rd rownot default
4th rownot default
5th rownot default

Common Values

ValueCountFrequency (%)
not default23364
77.9%
default6636
 
22.1%

Length

2021-10-08T03:30:22.947235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:23.033595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
default30000
56.2%
not23364
43.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SEX_V
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
female
18112 
male
11888 

Length

Max length6
Median length6
Mean length5.207466667
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
female18112
60.4%
male11888
39.6%

Length

2021-10-08T03:30:23.111809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:23.210581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
female18112
60.4%
male11888
39.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EDUCATION_V
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
university
14030 
graduate school
10585 
high school
4917 
other
 
468

Length

Max length15
Median length11
Mean length11.85006667
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuniversity
2nd rowuniversity
3rd rowuniversity
4th rowuniversity
5th rowuniversity

Common Values

ValueCountFrequency (%)
university14030
46.8%
graduate school10585
35.3%
high school4917
 
16.4%
other468
 
1.6%

Length

2021-10-08T03:30:23.298707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:23.375907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
school15502
34.1%
university14030
30.8%
graduate10585
23.3%
high4917
 
10.8%
other468
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MARRIAGE_V
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
single
15964 
married
13659 
divorce
 
323
others
 
54

Length

Max length7
Median length6
Mean length6.466066667
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowsingle
3rd rowsingle
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
single15964
53.2%
married13659
45.5%
divorce323
 
1.1%
others54
 
0.2%

Length

2021-10-08T03:30:23.482405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:23.567362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
single15964
53.2%
married13659
45.5%
divorce323
 
1.1%
others54
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SEX_female
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
18112 
0
11888 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Length

2021-10-08T03:30:23.667432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:23.732516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SEX_male
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
18112 
1
11888 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
018112
60.4%
111888
39.6%

Length

2021-10-08T03:30:23.805014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:23.879748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
018112
60.4%
111888
39.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EDUCATION_graduate school
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
19415 
1
10585 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Length

2021-10-08T03:30:23.947607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:24.010863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EDUCATION_high school
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
25083 
1
4917 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Length

2021-10-08T03:30:24.100499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:24.169946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EDUCATION_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29532 
1
 
468

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029532
98.4%
1468
 
1.6%

Length

2021-10-08T03:30:24.244097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:24.312721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
029532
98.4%
1468
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EDUCATION_university
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
15970 
1
14030 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Length

2021-10-08T03:30:24.387676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:24.448148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MARRIAGE_divorce
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29677 
1
 
323

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029677
98.9%
1323
 
1.1%

Length

2021-10-08T03:30:24.513731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:24.582981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
029677
98.9%
1323
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MARRIAGE_married
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
16341 
1
13659 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Length

2021-10-08T03:30:24.673352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:24.742408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MARRIAGE_others
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29946 
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029946
99.8%
154
 
0.2%

Length

2021-10-08T03:30:24.815473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:24.884094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
029946
99.8%
154
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MARRIAGE_single
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
15964 
0
14036 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Length

2021-10-08T03:30:24.950200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:25.017845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DEFAULT_NUM
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
23364 
1
6636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Length

2021-10-08T03:30:25.108032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:25.177118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NO_CONS_APR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
25105 
1
4895 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025105
83.7%
14895
 
16.3%

Length

2021-10-08T03:30:25.251672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:25.320397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
025105
83.7%
14895
 
16.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PAID_FULL_APR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
24260 
1
5740 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Length

2021-10-08T03:30:25.392997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:25.456611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_APR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
16286 
0
13714 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Length

2021-10-08T03:30:25.544870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:25.606111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NO_CONS_MAY
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
25454 
1
4546 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025454
84.8%
14546
 
15.2%

Length

2021-10-08T03:30:25.686852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:25.754466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
025454
84.8%
14546
 
15.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PAID_FULL_MAY
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
24461 
1
5539 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Length

2021-10-08T03:30:25.830234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:25.900699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_MAY
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
16947 
0
13053 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Length

2021-10-08T03:30:25.968812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:26.045315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NO_CONS_JUN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
25652 
1
4348 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025652
85.5%
14348
 
14.5%

Length

2021-10-08T03:30:26.123883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:26.196978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
025652
85.5%
14348
 
14.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PAID_FULL_JUN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
24313 
1
5687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Length

2021-10-08T03:30:26.267372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:26.333690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_JUN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
16455 
0
13545 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Length

2021-10-08T03:30:26.421984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:26.491383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NO_CONS_JUL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
25915 
1
4085 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025915
86.4%
14085
 
13.6%

Length

2021-10-08T03:30:26.566628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:26.635025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
025915
86.4%
14085
 
13.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PAID_FULL_JUL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
24062 
1
5938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Length

2021-10-08T03:30:26.717123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:26.789435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_JUL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
15764 
0
14236 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Length

2021-10-08T03:30:26.866169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:26.934305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NO_CONS_AUG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
26218 
1
3782 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026218
87.4%
13782
 
12.6%

Length

2021-10-08T03:30:27.003071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:27.070701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
026218
87.4%
13782
 
12.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PAID_FULL_AUG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
23950 
1
6050 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Length

2021-10-08T03:30:27.164836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:27.235052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_AUG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
15730 
0
14270 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Length

2021-10-08T03:30:27.309398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:27.375951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NO_CONS_SEP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
27241 
1
2759 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
027241
90.8%
12759
 
9.2%

Length

2021-10-08T03:30:27.456472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:27.528048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
027241
90.8%
12759
 
9.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PAID_FULL_SEP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
24314 
1
5686 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Length

2021-10-08T03:30:27.607838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:27.673928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_SEP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
15263 
1
14737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Length

2021-10-08T03:30:27.741601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:30:27.810129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PAY_DELAY_APR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2265666667
Minimum0
Maximum8
Zeros26921
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:27.874273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7154378198
Coefficient of variation (CV)3.157736441
Kurtosis19.93708244
Mean0.2265666667
Median Absolute Deviation (MAD)0
Skewness3.821385108
Sum6797
Variance0.5118512739
MonotonicityNot monotonic
2021-10-08T03:30:27.974004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
026921
89.7%
22766
 
9.2%
3184
 
0.6%
449
 
0.2%
746
 
0.2%
619
 
0.1%
513
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
026921
89.7%
22766
 
9.2%
3184
 
0.6%
449
 
0.2%
513
 
< 0.1%
619
 
0.1%
746
 
0.2%
82
 
< 0.1%
ValueCountFrequency (%)
82
 
< 0.1%
746
 
0.2%
619
 
0.1%
513
 
< 0.1%
449
 
0.2%
3184
 
0.6%
22766
 
9.2%
026921
89.7%

PAY_DELAY_MAY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2215
Minimum0
Maximum8
Zeros27032
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:28.077527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7177197137
Coefficient of variation (CV)3.240269588
Kurtosis21.31341111
Mean0.2215
Median Absolute Deviation (MAD)0
Skewness3.966570819
Sum6645
Variance0.5151215874
MonotonicityNot monotonic
2021-10-08T03:30:28.166732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
027032
90.1%
22626
 
8.8%
3178
 
0.6%
484
 
0.3%
758
 
0.2%
517
 
0.1%
64
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
027032
90.1%
22626
 
8.8%
3178
 
0.6%
484
 
0.3%
517
 
0.1%
64
 
< 0.1%
758
 
0.2%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
758
 
0.2%
64
 
< 0.1%
517
 
0.1%
484
 
0.3%
3178
 
0.6%
22626
 
8.8%
027032
90.1%

PAY_DELAY_JUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2587666667
Minimum0
Maximum8
Zeros26490
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:28.272207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.761112643
Coefficient of variation (CV)2.941308681
Kurtosis17.16067039
Mean0.2587666667
Median Absolute Deviation (MAD)0
Skewness3.547022193
Sum7763
Variance0.5792924553
MonotonicityNot monotonic
2021-10-08T03:30:28.363953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
026490
88.3%
23159
 
10.5%
3180
 
0.6%
469
 
0.2%
758
 
0.2%
535
 
0.1%
65
 
< 0.1%
12
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
026490
88.3%
12
 
< 0.1%
23159
 
10.5%
3180
 
0.6%
469
 
0.2%
535
 
0.1%
65
 
< 0.1%
758
 
0.2%
82
 
< 0.1%
ValueCountFrequency (%)
82
 
< 0.1%
758
 
0.2%
65
 
< 0.1%
535
 
0.1%
469
 
0.2%
3180
 
0.6%
23159
 
10.5%
12
 
< 0.1%
026490
88.3%

PAY_DELAY_JUL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3040666667
Minimum0
Maximum8
Zeros25787
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:28.467217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7905889977
Coefficient of variation (CV)2.600051516
Kurtosis10.47514326
Mean0.3040666667
Median Absolute Deviation (MAD)0
Skewness2.85658557
Sum9122
Variance0.6250309633
MonotonicityNot monotonic
2021-10-08T03:30:28.554395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
025787
86.0%
23819
 
12.7%
3240
 
0.8%
476
 
0.3%
727
 
0.1%
623
 
0.1%
521
 
0.1%
14
 
< 0.1%
83
 
< 0.1%
ValueCountFrequency (%)
025787
86.0%
14
 
< 0.1%
23819
 
12.7%
3240
 
0.8%
476
 
0.3%
521
 
0.1%
623
 
0.1%
727
 
0.1%
83
 
< 0.1%
ValueCountFrequency (%)
83
 
< 0.1%
727
 
0.1%
623
 
0.1%
521
 
0.1%
476
 
0.3%
3240
 
0.8%
23819
 
12.7%
14
 
< 0.1%
025787
86.0%

PAY_DELAY_AUG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3200333333
Minimum0
Maximum8
Zeros25562
Zeros (%)85.2%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:28.659295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8017273588
Coefficient of variation (CV)2.505137044
Kurtosis7.83849624
Mean0.3200333333
Median Absolute Deviation (MAD)0
Skewness2.598928087
Sum9601
Variance0.6427667578
MonotonicityNot monotonic
2021-10-08T03:30:28.751461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
025562
85.2%
23927
 
13.1%
3326
 
1.1%
499
 
0.3%
128
 
0.1%
525
 
0.1%
720
 
0.1%
612
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
025562
85.2%
128
 
0.1%
23927
 
13.1%
3326
 
1.1%
499
 
0.3%
525
 
0.1%
612
 
< 0.1%
720
 
0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
720
 
0.1%
612
 
< 0.1%
525
 
0.1%
499
 
0.3%
3326
 
1.1%
23927
 
13.1%
128
 
0.1%
025562
85.2%

PAY_DELAY_SEP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3567666667
Minimum0
Maximum8
Zeros23182
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:30:28.854807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7605941728
Coefficient of variation (CV)2.131909295
Kurtosis12.45880323
Mean0.3567666667
Median Absolute Deviation (MAD)0
Skewness2.809796725
Sum10703
Variance0.5785034957
MonotonicityNot monotonic
2021-10-08T03:30:28.933394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
023182
77.3%
13688
 
12.3%
22667
 
8.9%
3322
 
1.1%
476
 
0.3%
526
 
0.1%
819
 
0.1%
611
 
< 0.1%
79
 
< 0.1%
ValueCountFrequency (%)
023182
77.3%
13688
 
12.3%
22667
 
8.9%
3322
 
1.1%
476
 
0.3%
526
 
0.1%
611
 
< 0.1%
79
 
< 0.1%
819
 
0.1%
ValueCountFrequency (%)
819
 
0.1%
79
 
< 0.1%
611
 
< 0.1%
526
 
0.1%
476
 
0.3%
3322
 
1.1%
22667
 
8.9%
13688
 
12.3%
023182
77.3%

Interactions

2021-10-08T03:30:07.443215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:38.739181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:43.187629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:46.783929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:50.424070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:53.713761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:57.189664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:00.467598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:03.951368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:07.219814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:10.843633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:14.516613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:18.117492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:21.512424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:25.118322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:28.892693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:32.125243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:35.699357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:39.298475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:42.707204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:45.944525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:49.270069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:52.758211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:56.696716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:00.238135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:03.870537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:07.571890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:39.857145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:43.304729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:46.917551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:50.536187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:53.833196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:57.317043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:00.590915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:04.084095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:07.353083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:10.974505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:14.645032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:18.243309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:21.643039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:25.251957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:29.016309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:32.262143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:35.811769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:39.431456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:42.832302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:46.057598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:49.405770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:53.332825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:56.828495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:00.371478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:04.027927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:07.706009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:39.994607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:43.443316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:47.045287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:50.666132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:53.967357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:57.444485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:00.700894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:04.209128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:07.501200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:11.104214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:14.783510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:18.374985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:21.769331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:25.394044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:29.139761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:32.402872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:35.944887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:39.566330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:42.957273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:46.190371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:49.539901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:53.466930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:56.959930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:00.504833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:04.166520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:07.820772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:40.123553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:43.565688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:47.172725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:50.790576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:54.089684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:57.562460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:00.831568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:04.319097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:07.628885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:11.233519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:14.917204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:18.492624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:21.880008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:25.577667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:29.257010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:32.538981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:36.069040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:39.691329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:43.074341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:46.314210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:49.670520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:53.610005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:57.089170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:00.635644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:04.300479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:07.953477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:40.260361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:43.672406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:47.298122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:50.912509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:54.212550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:57.683170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:00.939403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:04.444735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:07.756053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:11.357169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:15.047785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:18.623763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:22.008070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:25.714353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:29.390971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:32.663455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:36.188359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:39.819457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:43.179174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:46.433931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:49.796763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:53.733421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:57.216488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:00.792017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:04.436660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:08.084961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:40.391439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:43.804937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:47.411915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:51.020527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:54.338736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:57.801549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:01.070287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:04.552227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:07.887082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:11.735369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:15.180121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:18.757367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:22.112099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:25.848650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:29.512848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:32.802290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:36.307692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:39.944294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:43.308668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:46.555673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-08T03:29:53.863641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:57.347208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:00.925282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-08T03:30:08.215787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:40.531070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-08T03:28:47.545531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:51.151830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:54.464309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:57.927560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-08T03:29:08.016572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:11.862292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-08T03:29:26.009218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:29.631431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-08T03:29:36.424836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:40.063139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:43.411286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:46.679028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-08T03:28:40.657622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:44.174000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-08T03:29:24.571341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:28.295954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:31.598693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:35.111712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:38.769277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:42.154470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:45.413980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:48.704138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:52.185326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:56.131953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:59.657483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:03.274158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:06.869518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:10.518081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:42.762390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:46.322786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:50.017093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:53.306399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:56.786977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:00.069599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:03.550081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:06.817616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:10.377895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:14.084092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:17.659922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:21.110207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:24.704073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:28.442351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:31.721529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:35.269902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:38.900077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:42.291260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:45.546484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:48.852594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:52.326369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:56.270682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:59.810854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:03.432757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:07.009887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:10.659538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:42.900937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:46.485319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:50.153425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:53.448660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:56.923727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:00.201262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:03.682965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:06.951580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:10.541360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:14.239408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:17.792606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:21.243504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:24.837322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:28.598193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:31.861845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:35.411455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:39.034608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:42.423115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:45.679755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:48.988765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:52.464061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:56.408036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:59.950675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:03.571238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:07.150306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:10.792318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:43.047593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:46.641379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:50.285761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:53.570063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:28:57.056749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:00.334763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:03.816369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:07.092140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:10.712046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:14.378604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:17.959846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:21.379050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:24.958617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:28.744871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:31.994288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:35.546821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:39.167887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:42.557145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:45.809184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:49.134610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:52.617126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:29:56.540452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:00.097549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:03.727916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:30:07.285119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-08T03:30:29.121248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-08T03:30:30.818326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-08T03:30:32.544269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-08T03:30:34.247659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-08T03:30:34.714845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-08T03:30:11.154751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-08T03:30:16.124626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

LIMIT_BALAGEPAY_SEPPAY_AUGPAY_JULPAY_JUNPAY_MAYPAY_APRBIL_AMT_SEPBIL_AMT_AUGBIL_AMT_JULBIL_AMT_JUNBIL_AMT_MAYBIL_AMT_APRPAY_AMT_SEPPAY_AMT_AUGPAY_AMT_JULPAY_AMT_JUNPAY_AMT_MAYPAY_AMT_APRDEFAULTSEX_VEDUCATION_VMARRIAGE_VSEX_femaleSEX_maleEDUCATION_graduate schoolEDUCATION_high schoolEDUCATION_otherEDUCATION_universityMARRIAGE_divorceMARRIAGE_marriedMARRIAGE_othersMARRIAGE_singleDEFAULT_NUMNO_CONS_APRPAID_FULL_APRREVOLVING_USE_APRNO_CONS_MAYPAID_FULL_MAYREVOLVING_USE_MAYNO_CONS_JUNPAID_FULL_JUNREVOLVING_USE_JUNNO_CONS_JULPAID_FULL_JULREVOLVING_USE_JULNO_CONS_AUGPAID_FULL_AUGREVOLVING_USE_AUGNO_CONS_SEPPAID_FULL_SEPREVOLVING_USE_SEPPAY_DELAY_APRPAY_DELAY_MAYPAY_DELAY_JUNPAY_DELAY_JULPAY_DELAY_AUGPAY_DELAY_SEP
0200002422-1-1-2-23913310268900006890000defaultfemaleuniversitymarried10000101001100100010010000000000022
112000026-120002268217252682327234553261010001000100002000defaultfemaleuniversitysingle10000100011000001001001000010200020
29000034000000292391402713559143311494815549151815001000100010005000not defaultfemaleuniversitysingle10000100010001001001001001001000000
35000037000000469904823349291283142895929547200020191200110010691000not defaultfemaleuniversitymarried10000101000001001001001001001000000
45000057-10-10008617567035835209401914619131200036681100009000689679not defaultmaleuniversitymarried01000101000001001001010001010000000
550000370000006440057069576081939419619200242500181565710001000800not defaultmalegraduate schoolsingle01100000010001001001001001001000000
650000029000000367965412023445007542653483003473944550004000038000202391375013770not defaultmalegraduate schoolsingle01100000010001001001001001001000000
7100000230-1-100-111876380601221-159567380601058116871542not defaultfemaleuniversitysingle10000100010010001001010010001000000
8140000280020001128514096121081221111793371933290432100010001000not defaultfemalehigh schoolmarried10010001000001001001000001001000200
92000035-2-2-2-2-1-1000013007139120001300711220not defaultmalehigh schoolsingle01010000010010010100100100100000000

Last rows

LIMIT_BALAGEPAY_SEPPAY_AUGPAY_JULPAY_JUNPAY_MAYPAY_APRBIL_AMT_SEPBIL_AMT_AUGBIL_AMT_JULBIL_AMT_JUNBIL_AMT_MAYBIL_AMT_APRPAY_AMT_SEPPAY_AMT_AUGPAY_AMT_JULPAY_AMT_JUNPAY_AMT_MAYPAY_AMT_APRDEFAULTSEX_VEDUCATION_VMARRIAGE_VSEX_femaleSEX_maleEDUCATION_graduate schoolEDUCATION_high schoolEDUCATION_otherEDUCATION_universityMARRIAGE_divorceMARRIAGE_marriedMARRIAGE_othersMARRIAGE_singleDEFAULT_NUMNO_CONS_APRPAID_FULL_APRREVOLVING_USE_APRNO_CONS_MAYPAID_FULL_MAYREVOLVING_USE_MAYNO_CONS_JUNPAID_FULL_JUNREVOLVING_USE_JUNNO_CONS_JULPAID_FULL_JULREVOLVING_USE_JULNO_CONS_AUGPAID_FULL_AUGREVOLVING_USE_AUGNO_CONS_SEPPAID_FULL_SEPREVOLVING_USE_SEPPAY_DELAY_APRPAY_DELAY_MAYPAY_DELAY_JUNPAY_DELAY_JULPAY_DELAY_AUGPAY_DELAY_SEP
29990140000410000001383251371421391101382624967546121600070004228150520002000not defaultmaleuniversitymarried01000101000001001001001001001000000
2999121000034322222250025002500250025002500000000defaultmaleuniversitymarried01000101001000000000000000000222223
299921000043000-2-2-28802104000000200000000not defaultmalehigh schoolmarried01010001000100100100001001001000000
29993100000380-1-10003042142710299670626694735500420001117844000300020002000not defaultmalegraduate schoolsingle01100000010001001001010010001000000
299948000034222222725577770879384775198260781158700035000700004000defaultmaleuniversitysingle01000100011000000000000000000222222
29995220000390000001889481928152083658800431237159808500200005003304750001000not defaultmalehigh schoolmarried01010001000001001001001001001000000
2999615000043-1-1-1-10016831828350289795190018373526899812900not defaultmalehigh schoolsingle01010000010001001010010010010000000
299973000037432-1003565335627582087820582193570022000420020003100defaultmaleuniversitysingle01000100011001001010000000000000234
2999880000411-1000-1-1645783797630452774118554894485900340911781926529641804defaultmalehigh schoolmarried01010001001010001001001010000000001
299995000046000000479294890549764365353242815313207818001430100010001000defaultmaleuniversitymarried01000101001001001001001001001000000

Duplicate rows

Most frequently occurring

LIMIT_BALAGEPAY_SEPPAY_AUGPAY_JULPAY_JUNPAY_MAYPAY_APRBIL_AMT_SEPBIL_AMT_AUGBIL_AMT_JULBIL_AMT_JUNBIL_AMT_MAYBIL_AMT_APRPAY_AMT_SEPPAY_AMT_AUGPAY_AMT_JULPAY_AMT_JUNPAY_AMT_MAYPAY_AMT_APRDEFAULTSEX_VEDUCATION_VMARRIAGE_VSEX_femaleSEX_maleEDUCATION_graduate schoolEDUCATION_high schoolEDUCATION_otherEDUCATION_universityMARRIAGE_divorceMARRIAGE_marriedMARRIAGE_othersMARRIAGE_singleDEFAULT_NUMNO_CONS_APRPAID_FULL_APRREVOLVING_USE_APRNO_CONS_MAYPAID_FULL_MAYREVOLVING_USE_MAYNO_CONS_JUNPAID_FULL_JUNREVOLVING_USE_JUNNO_CONS_JULPAID_FULL_JULREVOLVING_USE_JULNO_CONS_AUGPAID_FULL_AUGREVOLVING_USE_AUGNO_CONS_SEPPAID_FULL_SEPREVOLVING_USE_SEPPAY_DELAY_APRPAY_DELAY_MAYPAY_DELAY_JUNPAY_DELAY_JULPAY_DELAY_AUGPAY_DELAY_SEP# duplicates
02000024224444165016501650165016501650000000defaultmaleuniversitysingle010001000110000000000000000004444222
150000231-2-2-2-2-2000000000000not defaultfemalegraduate schoolsingle101000000101001001001001000000000012
250000261-2-2-2-2-2000000000000not defaultmaleuniversitysingle010001000101001001001001000000000012
38000025-2-2-2-2-2-2000000000000not defaultfemaleuniversitysingle100001000101001001001001001000000002
48000031-2-2-2-2-2-2000000000000not defaultfemaleuniversitymarried100001010001001001001001001000000002
58000042-2-2-2-2-2-2000000000000not defaultfemalehigh schoolmarried100100010001001001001001001000000002
690000311-2-2-2-2-2000000000000not defaultfemalegraduate schoolsingle101000000101001001001001000000000012
7100000491-2-2-2-2-2000000000000not defaultfemaleuniversitymarried100001010001001001001001000000000012
8110000311-2-2-2-2-2000000000000not defaultfemalegraduate schoolsingle101000000101001001001001000000000012
9140000291-2-2-2-2-2000000000000not defaultmalegraduate schoolsingle011000000101001001001001000000000012